Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.0
Journals
Entropy
Special Issues
Quantum Algorithms and Quantum Machine Learning
entropy-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Quantum Algorithms and Quantum Machine Learning

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1874

Special Issue Editor

Dr. Shenggen Zheng

E-Mail Website
Guest Editor
Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen, China
Interests: quantum algorithms; quantum machine learning; quantum circuit optimization; quantum circuit design automation; quantum finite automata

Special Issue Information

Dear Colleagues,

Quantum computing promises to fundamentally transform the way complex computational problems are formulated and solved. With rapid advances in quantum hardware and increasing access to noisy intermediate-scale quantum (NISQ) devices, the design of efficient quantum algorithms and the understanding of their computational complexity have become central topics in quantum information science. At the same time, quantum machine learning has emerged as a promising interdisciplinary area, aiming to combine the expressive power of quantum systems with data-driven learning paradigms.

This Special Issue focuses on recent theoretical and practical developments in quantum algorithms, quantum complexity, and quantum machine learning. It aims to provide a forum for high-quality contributions addressing algorithmic design, complexity analysis, and implementation-oriented optimization techniques. Topics of interest include, but are not limited to, quantum algorithmic frameworks, quantum learning models, quantum circuit optimization and design automation, quantum finite automata, and complexity-theoretic aspects of quantum computation. Particular attention is also given to resource-efficient methods suitable for NISQ-era devices, as well as rigorous analyses that deepen our understanding of quantum advantage.

By bringing together researchers from quantum computing, theoretical computer science, and machine learning, this Special Issue seeks to highlight emerging trends, identify open challenges, and stimulate cross-disciplinary collaboration. We welcome original research articles, reviews, and perspectives that advance the foundations and applications of quantum algorithms and quantum machine learning.

Dr. Shenggen Zheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum algorithms
  • quantum computational complexity
  • quantum machine learning
  • quantum circuit optimization
  • quantum circuit design automation
  • NISQ algorithms
  • variational quantum algorithms
  • quantum finite automata
  • quantum information processing
  • resource-efficient quantum computation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 12453 KB  
Article
Strategic Data Re-Uploads: A Pathway to Improved Quantum Classification Data Re-Uploading Strategies for Improved Quantum Classifier Performance
by Sara Aminpour, Yaser M. Banad and Sarah S. Sharif
Entropy 2026, 28(5), 550; https://doi.org/10.3390/e28050550 - 13 May 2026
Viewed by 298
Abstract
Quantum machine learning integrates quantum computing with classical machine learning techniques to enhance computational power and efficiency. A major challenge in quantum machine learning is developing robust quantum classifiers capable of accurately processing and classifying complex datasets. In this work, we present an [...] Read more.
Quantum machine learning integrates quantum computing with classical machine learning techniques to enhance computational power and efficiency. A major challenge in quantum machine learning is developing robust quantum classifiers capable of accurately processing and classifying complex datasets. In this work, we present an advanced approach leveraging data re-uploading, a strategy that cyclically encodes classical data into quantum states to improve classifier performance. We examine two cost functions, fidelity and trace distance, across various quantum classifier configurations, including single-qubit, two-qubit, and entangled two-qubit systems. Additionally, we evaluate four optimization techniques (L-BFGS-B, COBYLA, Nelder–Mead, and SLSQP) to determine their effectiveness in optimizing quantum circuits for both linear and non-linear classification tasks. Our results show that the choice of optimization method significantly impacts classifier performance, with L-BFGS-B and COBYLA often yielding superior accuracy. The two-qubit entangled classifier shows improved accuracy over its non-entangled counterpart, albeit with increased computational cost. Also, the two-qubit entangled classifier is the best option for real-world random datasets in terms of accuracy and computational cost. Linear classification tasks generally exhibit more stable performance across optimization techniques compared to non-linear tasks. Our findings highlight the potential of data re-uploading in quantum machine learning, outperforming existing quantum classifier models in terms of accuracy and robustness. This work contributes to the growing field of quantum machine learning by providing a comprehensive comparison of classification strategies and optimization techniques in quantum computing environments, offering a foundation for developing more efficient and accurate quantum classifiers. Full article
(This article belongs to the Special Issue Quantum Algorithms and Quantum Machine Learning)
►▼ Show Figures

Figure 1

12 pages, 2383 KB  
Article
Evaluating Photonic Quantum Memristors in Noisy Environments
by Jiachao Wang, Wentao Mao, Tengze Yang, Qiming Zhang and Wei Li
Entropy 2026, 28(5), 507; https://doi.org/10.3390/e28050507 - 1 May 2026
Viewed by 414
Abstract
While photonic quantum memristors (PQMs) offer promising avenues for neuromorphic computing, their performance is inherently affected by hardware noise, particularly photon loss and phase fluctuations. This study systematically investigates the impact of photon loss and phase fluctuations on PQM dynamics by employing the [...] Read more.
While photonic quantum memristors (PQMs) offer promising avenues for neuromorphic computing, their performance is inherently affected by hardware noise, particularly photon loss and phase fluctuations. This study systematically investigates the impact of photon loss and phase fluctuations on PQM dynamics by employing the noisy gates approach, which integrates dissipative effects directly into the device evolution. At the device level, we demonstrate that photon loss alters the dynamic trajectory of individual PQMs. It induces evident deformations in the characteristic pinched hysteresis loops, with the degradation of non-Markovian memory effects being particularly pronounced at shorter integration times. To further evaluate system-level implications, we construct a two-PQM network to execute the NARMA2 time-series prediction task. Under noiseless conditions, the network exhibits strong representation capabilities with a normalized mean square error (NMSE) of 0.0448. However, performance degrades markedly under incoherent evolution; the NMSE increases to 0.1552, 0.2567, and 0.3056 for photon loss probabilities of 0.2, 0.4, and 0.5, respectively. Furthermore, at a high photon loss probability of 0.5, extending the integration time fails to compensate for the degradation and instead exacerbates the prediction error. These findings indicate that photon loss impairs both individual device dynamics and network-level processing, emphasizing the critical need for loss-tolerant architectures in deploying PQM networks. Full article
(This article belongs to the Special Issue Quantum Algorithms and Quantum Machine Learning)
►▼ Show Figures

Figure 1

15 pages, 2002 KB  
Article
Analysis of Influencing Factors in Quantum Chemistry Simulation Based on VQE Algorithm
by Meng Zhang, Jian Kang, Qian Wu and Bing Han
Entropy 2026, 28(4), 440; https://doi.org/10.3390/e28040440 - 13 Apr 2026
Viewed by 674
Abstract
The Variational Quantum Eigensolver (VQE), as one of the most promising quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era, exhibits unique advantages in quantum chemistry simulations. It provides a novel approach to solving molecular electronic structure problems that are difficult to handle [...] Read more.
The Variational Quantum Eigensolver (VQE), as one of the most promising quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era, exhibits unique advantages in quantum chemistry simulations. It provides a novel approach to solving molecular electronic structure problems that are difficult to handle with classical computing. However, the performance of the VQE algorithm in quantum chemistry simulation is jointly affected by multiple factors, and its application in practical scenarios still faces numerous challenges. This paper first outlines the basic principles of the VQE algorithm and its core application scenarios in quantum chemistry simulation. Subsequently, it systematically analyzes the mechanism of the influencing factors, such as molecular system characteristics and algorithm parameter design, focusing on exploring how each factor specifically influences the results. Finally, the current research status and limitations in the optimization of influencing factors are summarized, and future research directions are proposed. This work aims to provide theoretical reference and technical support for improving the performance of quantum chemistry simulation based on the VQE algorithm and promoting its practical application. Full article
(This article belongs to the Special Issue Quantum Algorithms and Quantum Machine Learning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop